Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems.
I'm Hunter, co-founder of Powabase. We've been running an AI dev shop since ChatGPT first came out, and after many client projects we noticed the same pattern repeating itself. Nearly every AI-native app ends up needing the same stack: Postgres, a vector store, RAG pipelines, an agent runtime, memory, auth, and file storage.
Today you stitch that together from 6–8 tools, write a lot of glue code, and then watch your coding agent burn tokens navigating it. We've built ~100 production AI apps across regulated industries — finance, insurance, education, government — and the infra glue was always the slowest, most expensive part.
So we abstracted it into a unified backend. Powabase is the backend we wished we'd had — and now every new AI project we take on ships in a fraction of the time.
Powabase is that whole stack as one platform:
Postgres + pgvector + file storage, provisioned per project in one click
Standard Supabase features like auth and realtime
A context engineering layer with multiple RAG algorithms that hits 98.7% on FinanceBench
Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter
Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations
ReAct multi-agent orchestration with prebuilt tools (web search, database r/w, sandboxed code execution, etc.) and support for custom tool integrations via API and MCP
N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language
Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors
Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design
AI apps deserve their own backend abstraction, not a Frankenstein of generic infra + LLM wrappers. Supabase made Postgres easy to use; we want to do that for the full AI-native stack.
It's free to start, and our cookbook + example apps are open source on GitHub. We plan to open source a self-hosted version after early access period ends, likely around late June / July 2026.
I'll be in the comments all day with @tonyzhangcy , @xin_chen17 , and @michael_t_chang . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏
Early access users get free lifetime benefits — try it at app.powabase.ai and tell us what you build 🚀
About Powabase on Product Hunt
“Build AI apps with Postgres, RAG, and agents”
Powabase launched on Product Hunt on May 27th, 2026 and earned 274 upvotes and 41 comments, earning #2 Product of the Day. Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems.
On the analytics side, Powabase competes within Developer Tools, Artificial Intelligence and Database — topics that collectively have 984.6k followers on Product Hunt. The dashboard above tracks how Powabase performed against the three products that launched closest to it on the same day.
Who hunted Powabase?
Powabase was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Powabase including community comment highlights and product details, visit the product overview.
fmerian
Hey Product Hunt 👋
I'm Hunter, co-founder of Powabase. We've been running an AI dev shop since ChatGPT first came out, and after many client projects we noticed the same pattern repeating itself. Nearly every AI-native app ends up needing the same stack: Postgres, a vector store, RAG pipelines, an agent runtime, memory, auth, and file storage.
Today you stitch that together from 6–8 tools, write a lot of glue code, and then watch your coding agent burn tokens navigating it. We've built ~100 production AI apps across regulated industries — finance, insurance, education, government — and the infra glue was always the slowest, most expensive part.
So we abstracted it into a unified backend. Powabase is the backend we wished we'd had — and now every new AI project we take on ships in a fraction of the time.
Powabase is that whole stack as one platform:
Postgres + pgvector + file storage, provisioned per project in one click
Standard Supabase features like auth and realtime
A context engineering layer with multiple RAG algorithms that hits 98.7% on FinanceBench
Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter
Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations
ReAct multi-agent orchestration with prebuilt tools (web search, database r/w, sandboxed code execution, etc.) and support for custom tool integrations via API and MCP
N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language
Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors
Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design
AI apps deserve their own backend abstraction, not a Frankenstein of generic infra + LLM wrappers. Supabase made Postgres easy to use; we want to do that for the full AI-native stack.
It's free to start, and our cookbook + example apps are open source on GitHub. We plan to open source a self-hosted version after early access period ends, likely around late June / July 2026.
I'll be in the comments all day with @tonyzhangcy , @xin_chen17 , and @michael_t_chang . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏
Early access users get free lifetime benefits — try it at app.powabase.ai and tell us what you build 🚀